Distributed Processing/In-Network-Processing


Involved Staff


Sensor networks are of great interest for current research activities. Industry 4.0, environmental monitoring or autonomous vehicles are just a few application fields. In general, sensors transmit their measurement data, e.g. temperature or air pressure, to a central unit for further data processing. Key drawbacks of the central approach are e.g. that the central unit is a Single-Point-of-Failure/Single-Point-of-Trust and routing protocols are needed to forward measurement data to the central unit.

In-Network-Processing represents an alternative approach. By means of distributed processing the concept In-Network-Processing (INP) evaluates the data within the sensor network, and, thus no Single-Point-of-Failure/Single-Point-of-Trust is required to be used. In particular, each sensor executes some specific, but valuable information of the data processing. Information exchange guarantees the complete evaluation of the data to meet e.g. the data processing objective in mind such as the spatial distribution of a temperature field. Virtues of this distributed processing approach are a) high robustness against failures of communication connections, b) high security against wrong measurements or malicious adulteration (e.g., it is not sufficient to deliver wrong information to only one or two sensors) and c) the possibility of an autonomous operation.

A key challenge to be addressed is to balance the trade-off of the estimation quality required, but with low signaling/communication overhead.

In last years, the Department of Communications (ANT), has developed several methods and numerical algorithms of INP and has evaluated their application e.g., for 5G ultra-dense small cell networks (EU-project iJOIN) as well as to estimate diffusion fields for environmental monitoring (DFG-project INDA). The performed studies confirm the above mentioned virtues of INP.



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Last change on 10.09.2019 by M. Röper
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